from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-12 14:06:43.618985
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 12, Dec, 2020
Time: 14:06:47
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.6160
Nobs: 138.000 HQIC: -44.7493
Log likelihood: 1468.91 FPE: 1.69708e-20
AIC: -45.5251 Det(Omega_mle): 9.04190e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.457707 0.173981 2.631 0.009
L1.Burgenland 0.141512 0.084888 1.667 0.096
L1.Kärnten -0.291525 0.071723 -4.065 0.000
L1.Niederösterreich 0.109530 0.204779 0.535 0.593
L1.Oberösterreich 0.289287 0.170592 1.696 0.090
L1.Salzburg 0.174845 0.086393 2.024 0.043
L1.Steiermark 0.103248 0.121647 0.849 0.396
L1.Tirol 0.161895 0.081067 1.997 0.046
L1.Vorarlberg 0.001818 0.078433 0.023 0.982
L1.Wien -0.129002 0.163288 -0.790 0.430
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.518984 0.219258 2.367 0.018
L1.Burgenland 0.005440 0.106979 0.051 0.959
L1.Kärnten 0.338308 0.090388 3.743 0.000
L1.Niederösterreich 0.128273 0.258070 0.497 0.619
L1.Oberösterreich -0.194899 0.214987 -0.907 0.365
L1.Salzburg 0.196368 0.108876 1.804 0.071
L1.Steiermark 0.229097 0.153305 1.494 0.135
L1.Tirol 0.148365 0.102164 1.452 0.146
L1.Vorarlberg 0.202678 0.098844 2.050 0.040
L1.Wien -0.554682 0.205782 -2.695 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.308687 0.076035 4.060 0.000
L1.Burgenland 0.101175 0.037099 2.727 0.006
L1.Kärnten -0.017882 0.031345 -0.570 0.568
L1.Niederösterreich 0.119190 0.089495 1.332 0.183
L1.Oberösterreich 0.277296 0.074554 3.719 0.000
L1.Salzburg -0.004738 0.037756 -0.125 0.900
L1.Steiermark -0.038672 0.053164 -0.727 0.467
L1.Tirol 0.091408 0.035429 2.580 0.010
L1.Vorarlberg 0.129507 0.034277 3.778 0.000
L1.Wien 0.041280 0.071362 0.578 0.563
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.184721 0.087081 2.121 0.034
L1.Burgenland -0.000081 0.042488 -0.002 0.998
L1.Kärnten 0.030701 0.035899 0.855 0.392
L1.Niederösterreich 0.051878 0.102496 0.506 0.613
L1.Oberösterreich 0.378233 0.085385 4.430 0.000
L1.Salzburg 0.089620 0.043241 2.073 0.038
L1.Steiermark 0.201773 0.060887 3.314 0.001
L1.Tirol 0.034674 0.040576 0.855 0.393
L1.Vorarlberg 0.108161 0.039257 2.755 0.006
L1.Wien -0.081831 0.081729 -1.001 0.317
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.633615 0.187336 3.382 0.001
L1.Burgenland 0.077034 0.091404 0.843 0.399
L1.Kärnten -0.008585 0.077228 -0.111 0.911
L1.Niederösterreich -0.069143 0.220498 -0.314 0.754
L1.Oberösterreich 0.111608 0.183687 0.608 0.543
L1.Salzburg 0.038143 0.093024 0.410 0.682
L1.Steiermark 0.121875 0.130985 0.930 0.352
L1.Tirol 0.233674 0.087290 2.677 0.007
L1.Vorarlberg 0.029239 0.084453 0.346 0.729
L1.Wien -0.148843 0.175822 -0.847 0.397
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.182612 0.129444 1.411 0.158
L1.Burgenland -0.043096 0.063157 -0.682 0.495
L1.Kärnten -0.008293 0.053363 -0.155 0.876
L1.Niederösterreich 0.182440 0.152358 1.197 0.231
L1.Oberösterreich 0.384972 0.126922 3.033 0.002
L1.Salzburg -0.027713 0.064277 -0.431 0.666
L1.Steiermark -0.033907 0.090507 -0.375 0.708
L1.Tirol 0.194896 0.060315 3.231 0.001
L1.Vorarlberg 0.038118 0.058355 0.653 0.514
L1.Wien 0.139525 0.121488 1.148 0.251
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.220790 0.164117 1.345 0.179
L1.Burgenland 0.068780 0.080075 0.859 0.390
L1.Kärnten -0.072251 0.067657 -1.068 0.286
L1.Niederösterreich -0.075209 0.193169 -0.389 0.697
L1.Oberösterreich -0.091915 0.160920 -0.571 0.568
L1.Salzburg 0.012321 0.081495 0.151 0.880
L1.Steiermark 0.386818 0.114750 3.371 0.001
L1.Tirol 0.527726 0.076471 6.901 0.000
L1.Vorarlberg 0.224879 0.073986 3.039 0.002
L1.Wien -0.197820 0.154030 -1.284 0.199
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.110651 0.189975 0.582 0.560
L1.Burgenland 0.026113 0.092691 0.282 0.778
L1.Kärnten -0.085643 0.078316 -1.094 0.274
L1.Niederösterreich 0.163445 0.223604 0.731 0.465
L1.Oberösterreich 0.037578 0.186275 0.202 0.840
L1.Salzburg 0.221344 0.094335 2.346 0.019
L1.Steiermark 0.172641 0.132830 1.300 0.194
L1.Tirol 0.068411 0.088519 0.773 0.440
L1.Vorarlberg 0.026943 0.085643 0.315 0.753
L1.Wien 0.270554 0.178299 1.517 0.129
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.599012 0.104709 5.721 0.000
L1.Burgenland -0.015064 0.051089 -0.295 0.768
L1.Kärnten 0.001123 0.043166 0.026 0.979
L1.Niederösterreich -0.042918 0.123244 -0.348 0.728
L1.Oberösterreich 0.287175 0.102669 2.797 0.005
L1.Salzburg 0.008672 0.051995 0.167 0.868
L1.Steiermark 0.018440 0.073212 0.252 0.801
L1.Tirol 0.072426 0.048789 1.484 0.138
L1.Vorarlberg 0.176741 0.047204 3.744 0.000
L1.Wien -0.098285 0.098273 -1.000 0.317
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.108902 -0.002550 0.187322 0.241833 0.039990 0.082972 -0.126431 0.138968
Kärnten 0.108902 1.000000 -0.051317 0.182750 0.109250 -0.151715 0.184026 0.013818 0.267621
Niederösterreich -0.002550 -0.051317 1.000000 0.243727 0.052266 0.192601 0.092617 0.039482 0.367850
Oberösterreich 0.187322 0.182750 0.243727 1.000000 0.256090 0.264548 0.078579 0.061267 0.057491
Salzburg 0.241833 0.109250 0.052266 0.256090 1.000000 0.134233 0.043810 0.080823 -0.050235
Steiermark 0.039990 -0.151715 0.192601 0.264548 0.134233 1.000000 0.084867 0.068063 -0.167707
Tirol 0.082972 0.184026 0.092617 0.078579 0.043810 0.084867 1.000000 0.132906 0.110220
Vorarlberg -0.126431 0.013818 0.039482 0.061267 0.080823 0.068063 0.132906 1.000000 0.064967
Wien 0.138968 0.267621 0.367850 0.057491 -0.050235 -0.167707 0.110220 0.064967 1.000000